NCI Biomedical Informatics Blog
- The Promise and the Challenge of Deep Learning in Pathology
- Predictive Modeling for Pre-clinical Drug Screening: Improving Models Derived From Observational Studies Using Machine Learning and Simulation
- Population Level Pilot: Population Information Integration, Analysis, and Modeling for Precision Surveillance
- Introducing the Data Commons Framework
- Modeling the Dynamics of Membrane-bound Mutant RAS to Accelerate Discovery of Novel Drug Targets
JDACS4C Cellular Level Pilot for Predictive Modeling for Pre-clinical Screening
Goal: To provide a practical, scalable approach to in silico pre-clinical screening through advances in predictive modeling.
Description: This pilot will develop machine learning, large-scale data and predictive models based on experimental biological data derived from patient-derived xenografts. This will create a feedback loop, where the experimental models inform the design of the computational models. These predictive models may also point to new targets in cancer and help identify promising new treatments. This pilot is directly aligned with the Precision Medicine Initiative in oncology in that it leverages the models to screen personalized drug treatments for individual patients.
James Doroshow (NCI, Director, Division of Cancer Treatment and Diagnosis)
Yvonne Evrard (NCI, Frederick National Laboratory for Cancer Research)
Susan Holbeck (NCI, Division of Cancer Treatment and Diagnosis)
Rick Stevens (DOE, Argonne National Laboratory)
Frank Alexander (DOE, Los Alamos National Laboratory)